Deep convolutional neural network for preoperative prediction of microvascular invasion and clinical outcomes in patients with HCCs-Reference-Cited by-同舟云学术

Deep convolutional neural network for preoperative prediction of microvascular invasion and clinical outcomes in patients with HCCs

Published:2021-08-04 Issue:2 Volume:32 Page:771-782
ISSN:0938-7994
Container-title:European Radiology
language:en
Short-container-title:Eur Radiol

Author:

Li Xinming,Qi Zhendong,Du Haiyan,Geng Zhijun,Li Zhipeng,Qin Shuping,Zhang Xuhui,Liang Jianye,Zhang Xiao,Liang Wen,Yang Wei,Xie Chuanmiao,Quan Xianyue^ORCID

Funder

Guangdong Basic and Applied Basic Research Foundation

Clinical Research Starup Program of Southern Medical University by High-level University Construction Funding of Guangdong Provincial Department of Education

Opening Research Fund of Guangzhou Key Laboratory of Molecular and Functional Imaging for Clinical Translation

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s00330-021-08198-w.pdf

Reference38 articles.

1. Marrero JA, Kulik LM, Sirlin CB et al (2018) Diagnosis, staging, and management of hepatocellular carcinoma: 2018 practice guidance by the American Association for the Study of Liver Diseases. Hepatology 68:723–750

2. Zhou J, Sun HC, Wang Z et al (2018) Guidelines for diagnosis and treatment of primary liver cancer in China (2017 edition). Liver Cancer 7:235–260

3. Cai W, He B, Hu M et al (2019) A radiomics-based nomogram for the preoperative prediction of posthepatectomy liver failure in patients with hepatocellular carcinoma. Surg Oncol 28:78–85

4. Imamura H, Matsuyama Y, Tanaka E et al (2003) Risk factors contributing to early and late phase intrahepatic recurrence of hepatocellular carcinoma after hepatectomy. J Hepatol 38:200–207